Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Lijian Mei

Shenghui Huang

Jingmin Luo

Xiaolong Zhu

Yitong Wang

Kaining Huang

Yi Tong Wang

Chen Ran

Yugeng Lin

Mei Jiang

Haozhi Huang

Dianping Xu

Xiaoyi Jia

Haoyu Li

Yongsen Zheng

Minhui Wu

Huajie Huang

Yanhua Cheng

Xiao Long Zhu

Bolun Cai

Yike Liu

Jinchang Xu

Zhikang Tan

Wei Dong Chen

Jie Fan

Xing Ji

Min Luo

Yuan Fang

Kai Ning Huang

Ren Jian Chen

Wanyu Huang

Shao Bin Lin

Embodiments of this application disclose a headword extraction method performed by a computer device. The method includes: obtaining a sentence feature of a target sentence and word features of a plurality of words in the target sentence; extracting semantics of the sentence feature of the target sentence to obtain a semantic feature of the target sentence, and extracting semantics of the word feature of each word to obtain a semantic feature of the word; obtaining a respective matching degree between the semantic feature of each of the plurality of words and the semantic feature of the target sentence; and determining, among the plurality of words in the target sentence, a word with a largest corresponding matching degree as a headword of the target sentence. The method can improve the accuracy of headwords extracted from the target sentence.

Headword extraction method and apparatus, device, and storage medium

A method includes: obtaining a first image including a target item; selecting a plurality of reference images corresponding to the first image from a database; performing word segmentation on item text information corresponding to the plurality of reference images to obtain a plurality of words; and extracting a key word meeting a reference condition from the plurality of words, and determining the extracted key word as an item name of the target item.

Method and apparatus for determining item name, computer device, and storage medium

Embodiments of this application provide a neural network model deployment method, a prediction method and a device. The described features can implement deployment of a neural network model to improve the universality of the deployment of the neural network model to the terminal device by obtaining a layer definition and an operation parameter of each network layer of an initial neural network model, executing a target network layer corresponding to the network layers, applying relational connections amongst the target network layers using a net class, converting the operation parameters into a preset format, obtaining a target operation parameter based on the preset format, loading a corresponding target operation parameter in the target network layer, and obtaining a target neural network model based on the target operation parameter.

Neural network model deployment method, prediction method and related device

A video editing method, apparatus and storage medium are provided. The method includes obtaining an object, the object including one or more images; determining a content element of the object for video editing, the content element having a content type identifier; determining a material set identifier corresponding to the content type identifier according to a first behavior tree logic; determining a video editing material set corresponding to the material set identifier; and obtaining an edited video according to the content element and the video editing material set.

Video editing method, apparatus, and device, and storage medium

An image recognition method is provided. The method includes obtaining predicted locations of joints of a target person in a to-be-recognized image based on a joint prediction model, where the joint prediction model is pre-constructed by: obtaining a plurality of sample images; inputting training features of the sample images and a body model feature to a neural network and obtaining predicted locations of joints in the sample images outputted by the neural network; updating a body extraction parameter and an alignment parameter; and inputting the training features of the sample images and the body model feature to the neural network to obtain the joint prediction model.

Image recognition method and apparatus, electronic device, and readable storage medium using an update on body extraction parameter and alignment parameter

An image processing method and a related apparatus are provided. The method is applied to an image processing device, and includes: obtaining an original image, the original image including a foreground object; extracting a foreground region from the original image through a deep neural network; identifying pixels of the foreground object from the foreground region; forming a mask according to the pixels of the foreground object, the mask including mask values corresponding to the pixels of the foreground object; and extracting the foreground object from the original image according to the mask.

Image processing method and apparatus

An augmented reality processing method is provided for a terminal. The method includes: obtaining a plurality of frames of images, comprising a first image and a second image, which is a frame of an image immediately following the first image; obtaining a key point set of a first object in the first image; obtaining, through a neural network model, first pose key point sets respectively corresponding to a plurality of objects in the second image; determining a second pose key point set of the first object in the second image according to the key point set and a motion trend of the first object; using a target first pose key point set as a key point set of the first object in the second image; and generating an augmented information image according to the key point set of the first object in the second image.

Augmented reality processing method, object recognition method, and related device

A video image processing method and apparatus is described. The method includes obtaining a first key point of an Mframe image in a video. The method further includes performing weighted smoothing on the first key point of the Mframe image according to first key points in a historical key point queue and weights in a first target weight queue, to obtain a target key point. The historical key point queue includes a first key point corresponds to each frame image in N frame images. The N frame images are images before the Mframe image, N&#x3e;0. The weights in the first target weight queue corresponding to the first key points in the historical key point queue, A weight corresponding to a first key point of an (M-a)frame image being greater than or equal to a weight corresponding to a first key point of an (M-b)frame image, and a&#x3c;b. The method further includes adjusting the Mframe image according to the target key point.

Video image processing method and apparatus

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Shenzhen, China

Lijian Mei